---
title: "Inferring Complex Agent Motions from Partial Trajectory Observations"
authors:
  - "Finnegan Southey"
  - "Wesley Loh"
  - "Dana F. Wilkinson"
doi: ""
url: "https://ijcai.org/proceedings/2007/423"
pdf: "https://ijcai.org/proceedings/2007/0423.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "2631-2636"
track: ""
keywords:
---

# Inferring Complex Agent Motions from Partial Trajectory Observations

Finnegan Southey, Wesley Loh, Dana F. Wilkinson

*Proceedings of the Twentieth International Joint Conference on Artificial Intelligence*, pages 2631-2636.

## Abstract

Tracking the movements of a target based on limited observations plays a role in many interesting applications. Existing probabilistic tracking techniques have shown considerable success but the majority assume simplistic motion models suitable for short-term, local motion prediction. Agent movements are often governed by more sophisticated mechanisms such as a goal-directed path-planning algorithm. In such contexts we must go beyond estimating a target's current location to consider its future path and ultimate goal. We show how to use complex, black box motion models to infer distributions over a target's current position, origin, and destination, using only limited observations of the full path. Our approach accommodates motion models defined over a graph, including complex pathing algorithms such as A*. Robust and practical inference is achieved by using hidden semi-Markov models (HSMMs) and graph abstraction. The method has also been extended to effectively track multiple, indistinguishable agents via a greedy heuristic.